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Changes in Winter Warming Events in the Nordic Arctic Region
DAGRUN VIKHAMAR-SCHULER, KETIL ISAKSEN, AND JAN ERIK HAUGEN
Norwegian Meteorological Institute, Oslo, Norway
HANS TØMMERVIK
Norwegian Institute for Nature Research, FRAM–High North Research Centre for Climate and
the Environment, Tromsø, Norway
BARTLOMIEJ LUKS
Institute of Geophysics, Polish Academy of Sciences, Warsaw, Poland
THOMAS VIKHAMAR SCHULER
Department of Geosciences, University of Oslo, Oslo, Norway
JARLE W. BJERKE
Norwegian Institute for Nature Research, FRAM–High North Research Centre for Climate and
the Environment, Tromsø, Norway
(Manuscript received 28 October 2015, in final form 10 May 2016)
ABSTRACT
In recent years extreme winter warming events have been reported in arctic areas. These events are
characterized as extraordinarily warm weather episodes, occasionally combined with intense rainfall,
causing ecological disturbance and challenges for arctic societies and infrastructure. Ground-ice formation due to winter rain or melting prevents ungulates from grazing, leads to vegetation browning, and
impacts soil temperatures. The authors analyze changes in frequency and intensity of winter warming
events in the Nordic arctic region—northern Norway, Sweden, and Finland, including the arctic islands
Svalbard and Jan Mayen. This study identifies events in the longest available records of daily temperature
and precipitation, as well as in future climate scenarios, and performs analyses of long-term trends for
climate indices aimed to capture these individual events. Results show high frequencies of warm weather
events during the 1920s–30s and the past 15 years (2000–14), causing weak positive trends over the past 90
years (1924–2014). In contrast, strong positive trends in occurrence and intensity for all climate indices
are found for the past 50 years with, for example, increased rates for number of melt days of up to
9.2 days decade21 for the arctic islands and 3–7 days decade21 for the arctic mainland. Regional projections for the twenty-first century indicate a significant enhancement of the frequency and intensity of
winter warming events. For northern Scandinavia, the simulations indicate a doubling in the number of
warming events, compared to 1985–2014, while the projected frequencies for the arctic islands are up to 3
times higher.
1. Introduction
Denotes Open Access content.
Corresponding author address: Dagrun Vikhamar-Schuler,
Norwegian Meteorological Institute, P.O. box 43, Blindern, 0313
Oslo, Norway.
E-mail: [email protected]
DOI: 10.1175/JCLI-D-15-0763.1
Ó 2016 American Meteorological Society
Changes in extreme weather events will lead to increased stress on human and natural systems and a
propensity for serious adverse effects in many places
around the world (Maskrey et al. 2009, 2011). Recent
worldwide assessments of changes in climate extremes
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show great variability since 1950 across different regions
(Seneviratne et al. 2012).
The substantial arctic warming observed to have occurred since the midtwentieth century (Bindoff et al.
2013), which has intensified in the past decade (Walsh
et al. 2011), is much greater than for global or Northern
Hemisphere averages and is strongest for autumn and
winter seasons (Dicks et al. 2012; Cohen et al. 2014). The
reduction of sea ice and snow cover has especially contributed to the high-latitude warming (Walsh 2014). Of
great concern is that this arctic amplification is projected to
result in more winter warming events and, in particular,
more frequent and intensified rain-on-snow (ROS) events
(e.g., Rennert et al. 2009; Putkonen et al. 2009). Such
events can lead to the formation of thick internal ice layers
in the snowpack or ground ice, impacting the ground
vegetation and restricting access to winter fodder for
overwintering herbivores (e.g., voles, reindeer, or musk ox;
e.g., Forchhammer and Boertmann 1993; Putkonen and
Roe 2003; Kausrud et al. 2008; Bokhorst et al. 2009;
Hansen et al. 2013; Bjerke et al. 2014). For arctic societies,
such events may also have major impacts on, for example,
transportation and infrastructure (e.g., Hansen et al. 2014;
Stewart et al. 2015).
Although a number of studies have examined the
impact of these extreme events on environment and society, analyses of trends in such events in the arctic region appear challenging. The meteorological station
network in the Arctic is sparse, and there are relatively
few long-term records available of daily temperature
and precipitation measurements that can be used to
detect extreme events at higher latitudes (e.g., Moberg
et al. 2006; Rennert et al. 2009; Bokhorst et al. 2016). In
addition, precipitation measurements in the Arctic are
strongly influenced by wind-induced bias, which may
cause fictitious trends (Førland and Hanssen-Bauer
2000; Przybylak 2003).
A number of trend studies on warm winter events
have been performed on historical data, both observations and reanalysis data. Cohen et al. (2015) found
weak trends in ROS events over the Northern Hemisphere using reanalysis data from 1979 to 2014.
Groisman et al. (2003) assessed 50-yr trends in small
ROS events in the arctic winter and spring to find
an increasing trend in western Russia and a decrease in
western Canada. In Greenland, Pedersen et al. (2015)
reported large year-to-year variability in the number of
ROS events from 1979 to 2013, with the highest frequency in southwest Greenland where the events are
linked to foehn winds. Shabbar and Bonsal (2003) found
significant increases in both the frequency and duration
of warm spells over Canada during the second half of the
twentieth century. Hanesiak and Wang (2005) inferred
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an increase in freezing rain occurrence in parts of the
Canadian Arctic. Tuomenvirta et al. (2000) analyzed a
comprehensive dataset of monthly mean daily maximum
temperatures in the Nordic arctic region (NAR) and
found mainly positive trends during the period 1950–95.
On Svalbard, and more recently, Wei et al. (2016) found a
positive trend in warm extremes between 1975 and 2014
and Tomczyk and Bednorz (2014) reported a considerable increase in the number of warm days in the last decade (2001–10). Using a remote sensing dataset for the
winters 2000/01–2008/09, Bartsch et al. (2010) estimate an
average of one to three strong wintertime melt events per
season (November–February) in northern Scandinavia
and most areas on Svalbard.
However, there are few recent studies that have analyzed both long-term historical trends and future projected trends in winter warming and ROS events,
particularly in the Arctic. Sillmann et al. (2013) used
projections from global climate models (GCMs), but no
regional climate model (RCM) data, to study future
changes in extremes, and only part of the Arctic was
included (Greenland). Given that the Arctic is projected
to experience the greatest temperature change during
winter months (Dicks et al. 2012; Koenigk et al. 2012;
Manabe and Stouffer 1980; Overland et al. 2014), it is
likely that the Arctic will experience more winter
warming events in the twenty-first century. A better
understanding of extreme winter events in the Arctic is
then required, as stated by Dicks et al. (2012) and
Bokhorst et al. (2016).
In this paper, we identify and analyze changes in intensity and frequency of winter warming events over the
past 50–100 years, the present 15 years (2000–14), and
the future (twenty-first century) in the NAR. The NAR
is defined geographically as those parts of Norway,
Sweden, and Finland that are north of the Arctic Circle
(66.58N), as well as the arctic islands of Svalbard and Jan
Mayen (Fig. 1). A number of long-term continuous climatic monitoring series are available in the NAR (e.g.,
Kohler et al. 2006; Nordli et al. 2014), comprising a
unique record for studies related to Arctic climate
change and variability during winter. The region is a
primary pathway for the transport of atmospheric energy into the Arctic (Serreze et al. 2007) and is situated
in a projected hot spot area with increased heat fluxes
from the Atlantic water masses (e.g., Årthun et al. 2012),
the retreat of winter sea ice (Onarheim et al. 2014), and a
decrease in snow cover (e.g., Irannezhad et al. 2015).
These processes are connected with a relatively large
transition to more frequent temperatures above 08C
(e.g., Førland et al. 2011) and an increase in precipitation (e.g., Fleig et al. 2015) and heavy rainfalls
during winter (e.g., Hansen et al. 2014). This has
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VIKHAMAR-SCHULER ET AL.
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FIG. 1. Map of the Nordic arctic region and its location in
the Arctic (red line in the inset). Weather stations used in the
analysis are shown as black dots.
triggered extensive international environmental research, where many studies document the ecological
effects of winter warming events and ROS in the NAR
(e.g., Kohler and Aanes 2004; Hansen et al. 2013, 2014;
Stien et al. 2010, 2012; Hansen and Aanes 2012; Cooper
2014; Bjerke et al. 2014, 2015; Vikhamar-Schuler et al.
2013; Kivinen and Rasmus 2015; Rasmus et al. 2016),
and provides the motivation in this study for analyzing
both the past and future projected trends of such
extreme events.
To be relevant for the interdisciplinary environmental
research ongoing in the NAR, and as an arctic modification to the climate indices defined by the Expert
Team on Climate Change Detection and Indices (ETCCDI;
Karl and Easterling 1999; Klein-Tank et al. 2009; Sillmann
et al. 2013), we define warming events as extraordinarily
warm weather episodes occurring during the winter
season, occasionally combined with intense rainfall.
We define five different climate indices, described in
detail in section 2, to detect the occurrence of winter
warming events. The winter season is defined here as
comprising the months October–April. The total duration of this winter season corresponds closely to the
period during which mean monthly temperatures lie
below 08C, the climatological definition of winter
(Birkeland 1936, p. 25). Using this definition, the winter
season is slightly longer at the arctic islands (Fig. 2b) than
at the arctic mainland stations (Fig. 2a), but we choose to
use a consistent season duration to facilitate climate
comparisons across all stations. Plant species under the
snow cover have been observed to respond differently to
extraordinary warming events, depending on the timing of
FIG. 2. Mean monthly temperature for the recent period 1985–
2014 at (a) the arctic mainland stations and (b) the arctic stations.
Monthly values below 08C (gray line) are climatologically defined
as winter.
their occurrence during the winter season (Bokhorst et al.
2010, 2011; Preece and Phoenix 2014). Therefore, we also
consider three subperiods: 1) early winter (October–
November), 2) midwinter (December–February), and 3)
late winter (March–April). Throughout this paper, the
year denotes the year of the late winter season (e.g., the
2014 winter refers to October 2013–April 2014).
2. Data and study area
a. Weather station data
We selected data from a geographical distribution of
weather stations in the NAR that can capture the
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TABLE 1. Metadata for the daily observation series at the weather stations.
Station
Region
Temperature
Precipitation
Tromsø
Vardø
Karasjok
Abisko
Sodankylä
Svalbard Airport
Ny-Ålesund
Hornsund
Hopen
Bjørnøya
Jan Mayen
Troms, Norway
Finmark, Norway
Finnmark, Norway
Norrbottens Län, Sweden
Lapland, Finland
Svalbard
Svalbard
Svalbard
Svalbard
Svalbard
Jan Mayen
1867–present
1867–present
1877–present
1913–present
1908–present
1957–present
1974–present
1982–present
1945–present
1918–present
1922–present
1867–present
1894–present
1877–present
1913–present
1908–present
1957–present
1974–present
1982–present
1945–present
1920–present
1922–present
inherent variability from both maritime to continental
climates and feature long and high-quality observational
series. For computing statistics of the climate indices it is
crucial that the meteorological time series are homogeneous. Various nonclimatic factors can cause discontinuities and leave erroneous trends. Such factors include
relocation of stations, changes in observing procedures,
or new instruments.
A considerable number of weather stations within the
NAR are not appropriate for this study as they only have
short-term records of digitally available daily temperature and precipitation measurements, have significant
periods with missing data, have only single-parameter
observations, or have been relocated. For this analysis,
we selected the 11 longest available high-quality observation series with daily temperature and precipitation data (Table 1). These stations represent well
the climatic variability of the study area (Førland et al.
1997) and have been subject to extensive quality control
and inspection of metadata and have been homogenized
using modern statistical techniques (Frich et al. 1996).
Furthermore, the series are relatively long; seven stations
have more than 90 years of daily temperature and
precipitation data.
Data from the Norwegian stations (Tromsø, Vardø,
Karasjok, Svalbard Airport, Ny-Ålesund, Hopen,
Bjørnøya, and Jan Mayen) are freely available from the
climate database of the Norwegian Meteorological Institute (eKlima 2016). Note that the Svalbard Airport
temperature series, starting in 1898, is the only composite
series, which has been established by quality checking
and homogenizing the local series with the principal series from Svalbard Airport (Nordli 2010; Nordli et al.
2014). The Sodankylä dataset (Klein-Tank et al. 2002)
can be freely downloaded from the European Climate
Assessment and Dataset (European Climate Assessment
and Dataset 2016). The Abisko data series starts in 1913
(Kohler et al. 2006; Callaghan et al. 2010), while the records from Hornsund and Ny-Ålesund stations are rather
short, with observations starting in 1982 and 1974, respectively. These stations are included in the analysis to
extend the number of arctic stations.
Daily mean temperature values are used in our analysis. While daily maximum temperatures may be more
suitable for identifying single warming events, digitized
values of daily minima and maxima exist only for the
latest 40–80 years. Despite different methodologies used
to calculate daily mean temperature, both through time
and for the different Nordic countries (Ma and Guttorp
2013; Nordli and Tveito 2008), we consider the daily
mean temperature data series to be comparable for the
statistical evaluation.
Precipitation values are not homogenized. From 1876
to 1915, daily precipitation was measured at 0600 UTC
for the longest Norwegian time series—Tromsø, Vardø,
and Karasjok. During this period, the measured rainfall
was recorded at the previous day, except when the observer was confident that all the rainfall had fallen after
midnight. In such cases, the rainfall amount was recorded in the same day as it was measured (Harbitz
1963). From 1916 to the present rainfall amount was
recorded on the day it was measured (0600 UTC). The
changes in observational practices before and after 1916
for the three Norwegian series are thought to introduce
only a small bias in the dataset.
b. Regional climate model projections
We extracted daily mean temperature and precipitation from RCM simulations covering the study area
(Table 2). For the arctic mainland areas, RCM data are
available from the project ENSEMBLES (van der
Linden and Mitchell 2009). We selected 14 simulations
performed on a 25-km grid covering the time period
1961–2100. The ENSEMBLES simulations cover a
combination of four GCMs, seven regional climate
models, and two emission scenarios (IPCC 2000).
The northern border for the ENSEMBLES domain
lies just north of the Norwegian mainland, so the domain
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VIKHAMAR-SCHULER ET AL.
TABLE 2. RCM simulations from the ENSEMBLES project [notation adopted from Table 5.1 in van der Linden and Mitchell (2009)]
and the NorACIA project [below the blank line; notation adopted from Førland et al. (2009, 2011)]. Max Planck Institute (MPI),
Met Office Hadley Centre (HC), Nansen Environmental and Remote Sensing Center (NERSC), Centre National de Recherches
Météorologiques (CNRM), Royal Netherlands Meteorological Institute (KNMI), Swedish Meteorological and Hydrological Institute
(SMHI), Danish Meteorological Institute (DMI), Community Climate Change Consortium for Ireland (C4I), and Norwegian Meteorological Institute (METNO). (Acronym expansions are available online at http://www.ametsoc.org/PubsAcronymList.)
Period
GCM institute/model
RCM institute
RCM model
Emission scenario
Control
Scenario
Name
MPI/ECHAM5
MPI/ECHAM5
MPI/ECHAM5
MPI/ECHAM5
MPI/ECHAM5
NERSC/BCM
NERSC/BCM
CNRM/ARPEGE
HC/HadCM3, low
HC/HadCM3, std
HC/HadCM3, low
HC/HadCM3, high
HC/HadCM3, high
HC/HadCM, std
MPI
KNMI
SMHI
DMI
C4I
SMHI
METNO
DMI
SMHI
HC
HC
HC
C4I
METNO
REMO
RACMO
RCA3
HIRHAM5
RCA3
RCA3
HIRHAM
HIRHAM5
in RCA3
HadRM3
HadRM3
HadRM3
RCA3
HIRHAM
SRES A1B
SRES A1B
SRES A1B
SRES A1B
SRES A2
SRES A1B
SRES A1B
SRES A1B
SRES A1B
SRES A1B
SRES A1B
SRES A1B
SRES A1B
SRES A1B
1961–2000
1961–2000
1961–2000
1961–2000
1961–2000
1961–2000
1961–2000
1961–2000
1961–2000
1961–2000
1961–2000
1961–2000
1961–2000
1961–2000
2001–2100
2001–2100
2001–2100
2001–2100
2001–50
2001–2100
2001–2050
2001–2100
2001–2100
2001–2100
2001–2100
2001–2100
2001–2100
2001–2100
ENSEMBLES
MPI/ECHAM4
MPI/ECHAM5
HC/HadAM3H
HC/HadCM3, std
HC/HadCM3, std
HC/HadCM3, std
METNO
METNO
METNO
METNO
METNO
METNO
HIRHAM
HIRHAM
HIRHAM
HIRHAM
HIRHAM
HIRHAM
IS92a
SRES B2
SRES A2
SRES B2
SRES A1B
SRES A1B
1981–2010
1961–90
1961–90
1961–90
1961–90
1961–90
2021–50
2071–2100
2071–2100
2071–2100
2021–50
2071–2100
MPI92b
MPIB2
HADA2
HADB2
HADA1b
HADA1c
does not include the arctic islands. There instead we
used selected data from the Norwegian Arctic Climate
Impact Assessment (NorACIA) RCM simulations,
which cover the entire NAR study area, also on a 25-km
grid, and which have shown good results for daily
temperature and precipitation (Førland et al. 2009,
2011). We selected six NorACIA RCM simulations with
30-year time blocks covering the time periods 1961–90,
1981–2010, 2021–50, and 2071–2100 (Table 2, below the
blank line). The simulations cover a combination of one
RCM, four GCM and four emission scenarios. Both the
ENSEMBLES and NorACIA RCM simulations used in
this study were forced by global climate model data used
for analysis in previous IPCC reports (Cubasch et al.
2001; Meehl et al. 2007).
The imperfect GCM and RCM representations may
lead to unrealistic climate signal results. Therefore, the
coarse-resolution RCM data need to be statistically
processed to local stations using a bias-correction
method before carrying out the climate analysis (e.g.,
Takayabu et al. 2015). On one hand, this bias correction
unfortunately leads to loss of physical consistency between variables, but the present-day climate is much
closer to reality, which for impact studies has very high
priority (Sorteberg et al. 2014). To use our RCM data
for specific locations in the NAR, the data were bias
corrected and downscaled. Temperature and precipitation values were interpolated from the model grid
to station locations and adjusted for each site, using a
quantile-mapping procedure for each variable separately (Gudmundsson et al. 2012). The correction procedure takes into account new extremes in the tail of the
local climate distribution (e.g., increase in extreme
precipitation). Quantiles of the RCM data for the 1961–
2000 control period were mapped to those of observations from 1985–2014 (the most recent 30-yr reference
period used throughout this paper; Table 1). The transfer
function obtained was applied to correct the daily RCM
temperature and precipitation values. The present
procedure is evaluated and compared to other bias
correction methods in Sorteberg et al. (2014).
c. Study region climate
The climate of the NAR is thoroughly described by
Førland et al. (1997, 2009). Briefly, the NAR is exceptionally warm for its latitude, especially during winter.
The mild winters are mainly caused by two factors. First,
the region is frequently influenced by extratropical cyclones associated with the North Atlantic cyclone track.
Second, the Norwegian Atlantic Current acts as a conduit
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for warm and saline Atlantic Water from the North
Atlantic to the Barents Sea and Arctic Ocean
(Skagseth et al. 2008). About one-third of this flow
continues east on the Barents Sea shelf, while the larger
portion, the West Spitsbergen Current, flows toward
Spitsbergen (Schauer et al. 2004). The branches of the
Norwegian Atlantic Current lose heat to the atmosphere until their upper parts merge with and subduct
below the colder and fresher Arctic surface water in the
Arctic Ocean and create the northernmost area of open
water in the Arctic (Polyakov et al. 2005). The frequent
cyclones, the Atlantic Ocean, and the nearby coastal
waters have a moderating effect on the climate, especially in the maritime regions of northern Norway.
Tromsø and Vardø are coastal stations with mean
monthly midwinter (December–February) temperatures
higher than 258C, while the Abisko, Karasjok, and
Sodankylä stations are characterized by more continental
climates (midwinter temperatures below 2108C). The
stations at the arctic islands also represent two types of
climate (Fig. 2b). Bjørnøya and Jan Mayen are warmer
than Hopen, Svalbard Airport, Ny-Ålesund, and Hornsund,
with midwinter temperatures higher than 258C.
Winter precipitation is relatively variable in the study
area. The maritime stations (e.g., Tromsø) receive more
precipitation in winter than in summer, while the continental stations (Abisko, Karasjok, and Sodankylä)
receive less precipitation in winter than in summer
(Fig. 3a). Precipitation is distributed more evenly
through the year for the arctic islands but with a minimum amount in summer, just as for the maritime arctic
mainland stations (Fig. 3b).
Decadal temperature variability in the study area
(Fig. 4) shows annual mean temperature values
smoothed with a 10-yr Gaussian filter (Janert 2010). It is
clear that the arctic island stations have larger decadal
temperature variability than the arctic mainland stations. Furthermore, the long-term record can be broken
up into four basic periods characterized by 1) minimum
temperatures in the 1910s, 2) maxima in the 1930s and
1950s, 3) another minimum in the 1960s, and 4) a general
increase in the most recent decades. The strong temperature increase in the period from 1910 to the 1930s,
often referred to as the early twentieth-century warming
(EW20; Yamanouchi 2011), is strongest at Svalbard
Airport (Nordli et al. 2014). Wood and Overland (2010)
review the various theories on the causes of the EW20,
settling on natural climate variability combined with
anthropogenic forcing as the most likely factors, while
Yamanouchi (2011) attributes the EW20 mostly to
natural climate variability.
The most recent temperature increase at the arctic island
stations is much greater than that of the arctic mainland
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FIG. 3. Mean monthly precipitation sum for the recent period 1985–2014
at (a) the arctic mainland stations and (b) the arctic stations.
stations. Present temperatures are even significantly
higher than during any of the earlier periods at Svalbard
Airport (Førland et al. 2011; Nordli et al. 2014), while they
are slightly higher or at the same level as the 1930s maxima
for the arctic mainland stations.
3. Data analysis
a. Winter warm event indices
We defined five climate indices (Table 3) to identify
warming events. Indices 1–3 use only a temperature
threshold to identify warm days, while indices 4 and 5
additionally employ a precipitation threshold to identify
days that are both warm and wet.
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FIG. 4. October–April mean temperature deviation from the 1985–2014 reference period for
each station. Data are extracted from homogeneity-tested monthly temperature series and
filtered with a 10-yr Gaussian filter. These monthly series are longer than the digitized daily
data for the same stations. Arctic stations are shown in blue colors, while arctic mainland
stations are shown in red-yellow colors.
d
d
d
d
d
Index 1 counts the number of warm days (WD) in a
winter using a location-specific threshold temperature
defined as the 90th percentile (Beniston 2005; LópezMoreno et al. 2014) of the winter (October–April)
distribution for the most recent 30-yr reference period
(1985–2014) at each station.
Index 2 counts the number of melt days (MD) in a
winter using a fixed threshold temperature of 08C,
corresponding to the melting temperature for atmospheric conditions.
Index 3, the positive degree-day sum (PDD), is the
sum over the winter season of temperatures values
above the 08C threshold and represents a measure of
the intensity of the seasons warm events.
Index 4 is the number of melt and precipitation days
(MPD), similar to MD but with the additional constraint that precipitation P . 0 mm.
Index 5 accumulates the total winter precipitation
amounts (MPDsum) for the MPD.
Each index is computed from daily values in both the
observational and RCM datasets. Annual winter season
values of frequency and intensity are summarized for
each station.
To identify warming events with rainfall we used a
fixed threshold temperature of 08C (indices 4 and 5;
Table 3). In reality there is no constant or exact
temperature threshold differentiating precipitation
falling as rain or snow; this varies with atmospheric and
local conditions (Marks et al. 2013). To test the assumption of a fixed threshold temperature, we evaluated
available precipitation records where measured 12-h
precipitation has been visually classified as rain by the
observers in Tromsø (maritime) and Karasjok (continental) between 1960 and 2000. Results show that 99%
of winter rain events in Tromsø occurred at temperatures above 08C, while in the more continental climate of
Karasjok, 76% of the wintertime rain events occurred
above 08C.
b. Trends
To study trends, linear regressions of the indices are
analyzed over the past 50, 90, and 120 years, as well as
for the future 50–100 years. For comparison of the stations, trend values were expressed as absolute change
per decade for all periods, in addition to the change (%)
relative to the reference period 1985–2014 for the future
projections. Regression time periods were selected to
include a maximum number of stations; nine stations
have more than 50 years of observational data, seven
stations have more than 90 years of data, and four stations have more than 120 years. Trends for the past 90
years include both the recent warming period and the
TABLE 3. Climate indices used to analyze winter warming events. Index 1 uses variable threshold temperature for each station, while
indices 2–5 apply a fixed threshold temperature. The variable P is daily precipitation sum and T is daily mean temperature.
Number
Abbreviation
Name
Description
Type
1
2
3
4
5
WD
MD
PDD
MPD
MPDsum
Warm day
Melt day
Positive degree-day sum
Melt and precipitation day
Precipitation sum for MPDs
T . T90percentile
T . 08C
n
ådays51 (T . 08C)
T . 08C and P . 0 mm
n
ådays51 (T . 08C and P . 0 mm)
Frequency
Frequency
Intensity
Frequency
Intensity
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FIG. 5. Illustration of the data analysis. Computation of annual October–April warm days at
Karasjok station, using index 1. The plot shows linear trends for 50-, 90-, and 120-yr periods,
and a 10-yr Gaussian-filter smoothed line.
warm winters of the 1920s and 1930s. Trend values were
tested at a 5% significance level using the nonparametric
Mann–Kendall test, widely used for trend analysis of
climatological and hydrological time series (Yue and
Pilon 2004). The Mann–Kendall test does not require a
normal distribution of the data.
Decadal variability is studied by first computing change
(%) relative to the reference period 1985–2014 and then
applying a Gaussian 10-yr smoothing filter. Figure 5 illustrates, as an example, both the trend and the decadal
analysis for the historical data for Karasjok using index 1,
the annual October–April number of warm days.
c. Top five extreme winter warming years
To compare the current frequency of winter warming
events with that of the past, we identified the top five
extreme winters over the past 90 years for each climate
index and station (Table 4). We subdivided the 90-yr
period into 15-yr intervals and counted the occurrences
of extreme years in each of the intervals (Fig. 6). This
analysis is based on October–April periods, and subseasons were not considered.
4. Results
a. Winter warming events over the last 15 years
(2000–14) versus previous 15-yr periods
Figure 6 shows a histogram of the top five extreme
winters over the past 90 years. The stacked columns
show the contribution of each index to each 15-yr period. The sorted lists for each index are presented in the
Table 4. The highest number of top five extreme winters
(37%) occurred in the most recent 15-yr period (2000–14
winter seasons), followed by the 15-yr period of 1925–39
(20% of top five extreme winters). The lowest number of
top five extreme winters occurred during the 15-yr period of 1970–84 (5%). For a uniform frequency, one
would expect 17% of the events to fall into each 15-yr
period. The histogram demonstrates the high frequency
of extraordinary winter warming events occurring in the
present climate, compared to the past 90 years.
The percentage of 2000–14 years occurring on each of
the top five lists, individually, are 51% for melt and
precipitation days (index 4), 46% for melt days (index
2), 43% for positive degree days (index 3), 25% for
warm days (index 1), and 17% for precipitation sum for
melt and precipitation days (index 5).
The individual years appearing most often among the
top five years are 2006 (12 times), 1960 (10 times), 1990
(9 times), 1950 (8 times), and 2012 (7 times).
b. October–April winter warming events over the last
50–120 years
Trend analysis results are summarized in Table 5 for
the past 50 years (1964–2014), 90 years (1924–2014), and
120 years (1894–2014).
1) LINEAR TRENDS FOR THE PAST 50 YEARS
(1964–2014)
Overall all five winter warming indices show positive
trends for the past 50 years, with only a few exceptions,
and all are statistically significant (Table 5). In particular, index 1 has increased strongly for all arctic island
records, and the smallest increases, still significant, occurred at the arctic mainland stations. The rate of increase in warm days for the arctic islands varies from
3.2 days decade21 at Hopen to 5.5 days decade21 at Jan
Mayen. The rate of increase in warm days for the arctic
mainland stations varies from 2.2 (Karasjok and Tromsø)
to 3.1 days decade21 (Vardø).
1932
1959
1974
1974
1952
1970
1973
1938
1947
1932
1954
1999
2005
1963
1959
1957
1964
1932
2012
1974
1961
2008
2005
1992
1992
1983
1964
1988
2011
1987
1982
1987
2004
1934
1976
2008
1933
2012
1939
1989
2012
1930
2007
2014
2014
2000
1959
2010
2012
2012
1992
1949
1990
1990
1957
2006
2006
1949
2007
1950
1925
1954
1939
2014
1934
2006
1930
2012
2006
2003
1938
2013
1934
1938
2011
1937
1960
2004
1961
1938
1960
2002
2006
1953
2013
1934
2013
2011
1950
2004
2002
2011
1987
1960
1987
2013
2013
1939
2007
1959
1937
2005
2002
1957
2006
2014
2005
1960
2007
1959
1954
1939
1990
2004
1960
1990
1990
2006
1939
Vardø
Tromsø
Karasjok
Sodankylä
Abisko
Bjørnøya
Jan Mayen
2006
1930
1990
2006
2002
1954
2003
1950
1937
1950
2011
1925
1957
1947
2004
1960
1937
1950
1950
2010
1954
1960
1950
1989
1960
1959
1960
1955
2006
1930
1990
1990
1990
2006
2003
2012
1932
1950
1989
1925
2010
1930
1930
2006
2002
2014
2003
1960
1994
2008
1957
1937
2013
1937
1934
1934
4
3
2
4
3
2
4
3
2
1
5
4
3
Index 2: Melt days
2
4
3
2
1
Index 1: Warm days
5
1
Index 3: PDD
5
1
Index 4: MPD
5
1
Index 5: MPDsum
5
VIKHAMAR-SCHULER ET AL.
Station
TABLE 4. Top five list of extreme years over the past 90 years. Index 1: number of warm days; index 2: number of melt days; index 3: positive degree-day sum; index 4: number of melt and
precipitation days; and index 5: precipitation sum for melt and precipitation days. Years 2000–14 are in boldface font.
1 SEPTEMBER 2016
6231
The temperature threshold for index 1 varies for
each station and emphasizes therefore local changes in
extremes. Indices 2–5 are all based on fixed temperature and precipitation thresholds (08C and 0-mm precipitation) and generally show similar results, with the
strongest positive trends for stations with average
winter temperatures near 08C (Jan Mayen, Vardø,
Bjørnøya, and Tromsø; Figs. 2a,b). With increasing
winter temperatures, the probability for winter days
above 08C is more likely to increase, compared to
colder stations.
Jan Mayen has the strongest trends for both the
frequency indices 2 and 4 and the intensity indices
3 and 5. The station experienced an increase by 9.2
melt days per decade and an increase by 7.5 melt
days with precipitation per decade. On Jan Mayen,
seasonal temperature variations are very small with
average winter temperatures a few degrees below
08C. Thus, a general temperature increase will have a
significant impact on the frequency and intensity in
winter warming events. In addition, because of the
island’s location at the border between the relatively
warm Norwegian Sea and the cold Greenland Sea,
temperatures and precipitation patterns are sensitive to changes in oceanic and atmospheric circulation patterns. The smallest changes for indices 2–5
were found for Svalbard Airport, Hopen, and
Abisko.
Winter warming events are getting not only warmer
but also wetter (Table 5). At all stations, winter warm
precipitation sums are increasing, with Jan Mayen and
Tromsø stations having the strongest positive trends.
These two stations also have the largest winter precipitation among the island and mainland stations, respectively (Fig. 3).
2) LINEAR TRENDS FOR THE PAST 90 (1924–2014)
AND 120 (1894–2014) YEARS
There are weak positive trends for most stations for all
five indices in the past 90 years. However, only a few
trends are significant. Jan Mayen even exhibits weak
negative trends (not significant) over the past 90 years
for two of the five indices (indices 1 and 3; Table 5).
Winters during the 1920s–30s were warm throughout the
NAR, representing a period that most closely resembles
the present (Fig. 4).
Vardø, Tromsø, and Karasjok are the only stations
with more than 120 years of observations. Trend
values for changes in warming events at these stations
show weak positive trends for all indices. However,
Vardø is the only station with significant positive
trends for both frequency and intensity indices over
the past 120 years.
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FIG. 6. Histogram of the top five extreme winters summarized for the five climate indices occurring in six 15-yr periods from 1924 to 2014. The stacked columns show the contribution of each
index to each 15-yr period. For a uniform frequency one would expect 17% (gray line) of the events
to fall into each 15-yr period. Of the top five years, 37% occur in the most recent 15-yr period.
c. Early, mid-, and late winter warming events over
the last 50–120 years
Early winter (October–November), midwinter
(December–February), and late winter (March–April)
warming events over the past roughly 100 years for the
five indices are shown in Figs. 7–11.
For early winter, some arctic stations have a maximum
period in the 1950s (index 1: Jan Mayen, Hopen, and
Bjørnøya), while the other stations and indices follow
the general trends in the long-term temperature development (Fig. 4).
For midwinter the results for all indices show that the
frequency and intensity of warming events during the
1930s were high and at approximately the same maxima
level as the present level (last 15 years), both for arctic
island and arctic mainland stations. In addition, mainland
stations reached a maximum in the 1990s, which is
not observed for the arctic islands. Several stations experienced a period of minimum midwinter warming
events from approximately 1965 to 1970.
The trends in frequency of late winter warming events
follow the general long-term temperature development
with an increase over the past 50 years (Fig. 4). For some
of the stations there are large deviations—for example,
for Ny-Ålesund in the 1970s and 1980s and for Hopen in
the 1980s where both have peaks during late winter with
large amounts of precipitation during the warming
events (Fig. 11). Most stations have a maximum period
around 2005 for all five indices.
d. Winter warming events over the next 50–100 years
Linear trends of winter warming events for the five
indices from 2000 to 2100 are summarized in Table 5. All
stations show positive future trends equal to or higher
than the observed trends during the last 50 years. For
all indices, the largest relative change will take place
at the six arctic island stations, ranging from 317%
(Hopen) to 201% (Svalbard Airport) for index 1. The
relative increase for the arctic islands is larger for index
2, ranging from 402% (Hopen) to 118% (Jan Mayen).
For the same indices, the relative change is smaller for
the arctic mainland stations, ranging from 211%
(Vardø) to 115% (Abisko) for index 1 and from 113%
(Vardø) to 64% (Tromsø) for index 2. Roughly
250 (147,340)
245 (183,307)
196 (152,252)
514 (438,607)
173 (157,194)
119 (118,124)
190 (164,218)
69 (55,87)
180 (137,196)
171 (155,164)
153 (132,173)
11.3
4.2
14.4
13.5
12.3
16.0
18.7
17.8
5.6
6.0
8.3
—
—
—
—
—
—
3.1
0.4
0.7
—
—
—
—
—
—
1.6
1.5
3.7
3.5
0.2
0.1
1.4
—
2.6
—
2.4
14.1
20.8
11.9
20.5
2.9
1.3
11.9
262 (186, 324)
225 (167, 280)
206 (150, 261)
300 (258, 346)
122 (107, 143)
86 (83, 90)
117 (91, 130)
57 (50, 68)
115 (100, 119)
98 (98, 108)
103 (90, 118)
2.4
2.0
3.8
4.4
4.4
4.0
5.2
3.1
2.2
1.9
2.3
—
—
—
—
—
—
2.4
1.3
0.8
—
—
—
—
—
—
1.5
0.9
2.1
0.4
0.8
0.2
0.6
—
1.3
—
1.5
5.1
7.5
6.1
3.2
2.3
1.5
1.3
585 (461, 712)
472 (321, 585)
582 (472, 682)
1064 (844, 1222)
535 (327, 687)
268 (199, 320)
243 (211, 271)
139 (116, 160)
207 (158, 243)
162 (132, 194)
184 (157, 216)
12.1
13.6
17.5
31.1
46.5
31.8
49.2
36.6
21.4
20.0
21.6
—
—
—
—
—
—
4.3
2.0
1.2
—
—
—
—
—
—
1.3
21.5
4.4
1.6
2.7
1.2
0.3
—
3.5
—
2.8
10.2
17.7
16.3
17.8
6.7
9.4
9.2
322 (261, 370)
263 (191, 316)
280 (245, 323)
402 (367, 457)
177 (147, 207)
118 (106, 126)
113 (106, 134)
64 (60, 68)
115 (102, 125)
89 (86, 101)
103 (99, 111)
4.2
4.3
6.2
8.8
9.4
7.8
8.8
5.7
4.6
4.2
4.5
—
—
—
—
—
—
2.1
0.7
0.6
—
—
—
—
—
—
1.5
0.2
2.2
0.6
0.8
0.6
1.2
—
2.1
—
2.1
6.7
9.2
7.2
5.1
2.5
3.2
3.1
219 (178, 254)
201 (150, 243)
252 (219, 293)
317 (294, 358)
300 (230, 353)
250 (207, 280)
211 (193, 248)
152 (135, 165)
134 (118, 147)
115 (106, 132)
126 (116, 138)
5.5
5.2
6.5
9.4
9.3
7.8
7.9
4.9
4.2
3.7
4.1
—
—
—
—
—
—
0.9
0.3
0.4
—
—
50
—
3.6
—
3.2
3.4
5.5
3.1
2.2
2.2
2.6
3.0
Ny-Ålesund
Svalbard-A.
Hornsund
Hopen
Bjørnøya
Jan Mayen
Vardø
Tromsø
Karasjok
Abisko
Sodankylä
—
—
—
—
0.6
20.6
0.8
0.2
0.7
0.4
0.8
21C (%)
50 90 120 21C
21C (%)
50 90 120 21C
21C (%)
VIKHAMAR-SCHULER ET AL.
Station
90 120 21C
21C (%)
50 90 120 21C
21C (%)
50
90 120 21C
Index 5: MPDsum (mm decade21)
Index 4: MPD
Index 3: PDD (8C decade21)
Index 2: Melt days
Index 1: Warm days
TABLE 5. October–April change per decade in 1) number of days (indices 1, 2, and 4), 2) positive degree-day sum (index 3), and 3) precipitation sum (index 5). The columns show the
linear trend per decade for the last 50, 90, and 120 years, as well as the next 100 years (21C). Last column shows the relative increase (%) from 2000 to 2100. The 10th and 90th percentiles
of future model spread are shown in parentheses. Significant trend values are marked in boldface on historic data. Stations are ordered by latitude from north to south, with arctic
mainland stations in italic font.
1 SEPTEMBER 2016
6233
speaking, the relative increase is more than double for
the arctic mainland stations and up to 3 times for the
arctic island stations by 2100.
Detailed downscaled scenarios for melt days and
precipitation sum are shown in Figs. 12a–d for the
northernmost (Ny-Ålesund) and the southernmost
(Sodankylä) stations in our study area. The spread in the
ENSEMBLES RCM data is described by the minimum
and maximum, the 10th and 90th percentiles of all
members, while the NorACIA RCM data are plotted as
annual values. Figures 12e,f show the temperature and
precipitation data used for the computation of the indices for Sodankylä, where both sources of model data
are available. The spread and main trend of the observations are well captured by the model ensemble for the
historical period 1961–2014, but a year-to-year correspondence of modeled and observed data is not expected. Based on these results, we consider the locally
downscaled scenarios to have reasonable quality to
perform weather statistics and trend analysis using the
indices.
5. Discussion
a. Winter warming trends in the twentieth century
1) SEASONAL AND DECADAL VARIABILITY
There is a strong maximum in winter warming events
for the midwinter period during the 1930s, across all
stations; however, this is less pronounced for the early
and late winter trends. This is the signature of the
EW20 (Brönnimann 2009; Wood and Overland 2010;
Yamanouchi 2011) discussed in section 2c.
The 1990s had a midwinter warming maximum period
at arctic mainland stations but not at arctic island stations. During this period, many maritime glaciers in
Norway advanced, while most glaciers worldwide retreated (Andreassen et al. 2005; Chinn et al. 2005). The glacier
advance was caused by a series of precipitation-rich
winters. Chinn et al. (2005) related the increase of winter precipitation to stronger westerly atmospheric circulation transporting more moist air toward land.
Since only arctic mainland stations and not the arctic
islands experienced a maximum in winter warming
events in this period, the results may be related to more
southern storm tracks affecting the arctic mainland
of the Nordic arctic region.
Around 2005/06 there is a maximum in warm events
for most of the stations for all the indices (except index
5) and for all seasons. The maximum is generally
slightly stronger for the arctic island stations than the
arctic mainland stations. The winter of 2006 also appears most often in the list of top five extreme winters
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FIG. 7. Change (%) in number of warm days (index 1) relative to the 1985–2014 mean. Arctic
stations in blue and arctic mainland stations in red–yellow. (a) Early winter, (b) midwinter, and
(c) late winter.
(12 occurrences in Table 4). During this particular
winter the NAR experienced an unusual synoptic
situation with long periods of strong, mild southerlies. Sea temperatures were unusually high with
anomalous ocean heat transport observed west of
Svalbard (Walczowski and Piechura 2011). The normal
cycle of sea ice formation was interrupted, causing
unusually large regions with open water around Svalbard (Cottier et al. 2007). The associated high moisture
flux resulted in, for instance, exceptionally high winter
accumulation at Austfonna, the largest ice cap at
Svalbard (Dunse et al. 2009). During this winter a
distinct shift of the sea ice cover on two fjords near
Svalbard Airport and Hornsund was observed, from
generally ice-covered fjords before 2006 to ice-free
fjords after 2006 (Muckenhuber et al. 2016). In turn,
high temperatures, including winter warming events,
were then recorded on land. Mean air temperature from
December 2005 to May 2006 at Svalbard Airport was
8.28C above the 1961–90 average, representing the
warmest temperature anomaly since the start of this
time series (Isaksen et al. 2007).
The observed seasonal and decadal changes shown
here are probably a consequence of how the winter sea
ice extent is tied to temperature and precipitation at the
arctic islands stations. Cold and dry winters are associated with increased sea ice extent near the stations,
while ice-free winters are typically mild and humid
(Førland et al. 2009). North of Svalbard, sea ice cover
has been reduced more in winter (210% decade21) than
in summer (26% decade21) since 1979 (Onarheim et al.
2014). This is in contrast to the entire Arctic where the
largest seasonal reduction in sea ice extent is observed in
September, a 13% reduction per decade since 1979
1 SEPTEMBER 2016
VIKHAMAR-SCHULER ET AL.
6235
FIG. 8. As in Fig. 7, but for melting days (index 2).
(Stroeve et al. 2007, 2012). Although there is a general
negative trend in sea ice extent, the interannual variability is largely due to variations in ocean and atmosphere circulation and heat transfer.
2) STRONG 50-YR TRENDS AND WEAK 90- AND
120-YR TRENDS
Regardless of the season (early, mid-, and late winter), our results showed weak winter warming trends
over the past 90 years (from 1924 to 2014) and 120 years
(1894–2014), while strong trends are found for all stations over the past 50 years (from 1964 to 2014). The
50-yr trends are clearly increasing with latitude within
our study area and are probably linked to the general
temperature development in the area. Arctic temperatures increased by 0.098C decade21 for the twentieth
century, which is more than the temperature increase
of 0.068C decade21 for the entire Northern Hemisphere
over the same period (Berner et al. 2005). Limiting the
period to the past 50 years, the arctic temperature increase was much stronger. The Arctic is now warming at
more than twice the rate of lower latitudes, and mean
annual air temperature has increased by 2.38C since the
1970s (0.58–0.68C decade21; Overland et al. 2015). This is
also clearly observed the past two decades at Svalbard
with the strongest temperature increase in winter, by 28–
38C decade21 (Førland et al. 2011).
Our results show that the 1930s, the 1990s, and the
recent 15-yr period are maxima in the Arctic. The causes
of the two latter periods are thought to be different
from the first. The EW20 presumably was governed
by natural variability (Wood and Overland 2010;
Yamanouchi 2011). In contrast, the recent warming is
associated with increased anthropogenic greenhouse
gas forcing, resulting in increased cyclone activity in the
NAR (Sorteberg and Walsh 2008; Førland et al. 2009). Both
the number and the intensity of cyclones crossing 708N
increased from 1950 to 2007, particularly during
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FIG. 9. As in Fig. 7, but for positive degree-day sum for the melt days (index 3).
midwinter (December–February), where positive
trends were found for both the mean intensity of the
cyclones and the intensity of the most extreme
cyclones.
3) WARMER AND WETTER AFTER YEAR 2000?
As discussed in the previous section, the arctic temperature amplification has been strong over the past
15 years (2000–14), particularly during autumn and winter (Serreze and Barry 2011; Serreze and Stroeve 2015).
Our analysis is in line with these observations showing
that the highest number of top five extreme winter
warming years have occurred within the most recent 15-yr
period (2000–14), particularly the climate indices 2–4
(Fig. 6).
b. Winter warming trends in the twenty-first century
For future scenarios, the large increase in winter warming events for the arctic island stations is probably strongly
influenced by the GCM modeled sea ice extent, which is
shown to exert a significant control on the climate of
Svalbard (e.g., Benestad et al. 2002; Day et al. 2012). In
addition, the predicted large relative increase of warming
events in winter for the arctic islands is also a result of
normalizing using current conditions, which feature relatively few events per year, even if the absolute increase in
events predicted is not too different from projections for
the arctic mainland stations.
Another possible explanation for larger increases
predicted for the arctic island stations is that the ensemble is based on fewer realizations (only NorACIA
data) than for the mainland region (both ENSEMBLES
and NorACIA data). A smaller ensemble is usually
associated with a smaller spread in the results. But
here the NorACIA simulations include both moderate and more extreme emission scenarios (e.g.,
SRES A2), whereas the ENSEMBLES data are
mainly based on one emission scenario (SRES A1B;
van der Linden and Mitchell 2009; Førland et al.
2009, 2011).
1 SEPTEMBER 2016
VIKHAMAR-SCHULER ET AL.
6237
FIG. 10. As in Fig. 7, but for the number of melt and precipitation days (index 4).
The tabulated indices for the twenty-first century
were based on bias-corrected model data, with calculation of combined indices from independently
modified parameters. The analysis was repeated from
uncorrected model data in order to check the sensitivity to the quantile-mapping procedure. Although
the quantitative responses slightly differ for some of
the stations, the major conclusion is that both analyses
(corrected vs original data) show large increases for
arctic islands stations. On the other hand, the simulation of the climate at arctic islands stations is influenced by the quality of the forcing data from
the global climate models and their ability to model
arctic sea ice and sea surface temperature as well as
relatively low-resolution forcing data over sea. The
ability of the regional climate models to simulate
temperatures around the threshold temperature (08C)
can also influence the results. Nevertheless, trends for
all indices are similar for both datasets (NorACIA and
ENSEMBLES), and the expected large increase predicted for the twenty-first century is likely due to the
response in the forcing GCM data.
c. Impact of warmer wetter winters on ecosystems,
soil, and society
Warmer and wetter winters will have major implications for northern European, arctic biomes and societies. Northern terrestrial ecosystems are adapted to a
long-lasting snow cover. Hence, in regions where climate change will lead to reduced snow cover, especially
plants and animals in the subnivean environment will be
particularly adversely disturbed (Bjerke et al. 2014;
Williams et al. 2015). Organisms overwintering above
the snow will also be severely impacted in various ways
(Bjerke et al. 2014; Hansen et al. 2014). Records from
warm, wet years in the NAR illustrate these impacts.
The winter of 2006 was the individual winter appearing
most often among the top five extreme winters in our
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FIG. 11. As in Fig. 7, but for the precipitation sum for melt and precipitation days (index 5).
data. In their online supplementary information, Bjerke
et al. (2014) summarized the biological impacts of
weather events this winter on the mainland: there was
extensive damage to evergreen plants, both dwarf
shrubs and coniferous trees, due to frost drought, which
probably took place during a cold late winter period
after the winter warming events. The warming events
this winter were associated with strong winds; windborne salt spray caused some damage to plants, and
wind felling was also widespread. In Svalbard, ROS
events this winter led to ground-ice accumulation and
population crashes of reindeer and voles (Stien et al.
2012). The winter of 2012 also appears on the top-five list
in our analysis. Hansen et al. (2014) show that the warm
weather this winter led to a similar situation as in 2006
with numerous ROS events, ground-ice accumulation,
and starvation-induced mortality in all monitored
populations of wild reindeer by blocking access to
the winter food source. Similar population crashes to
semidomesticated reindeer caused by ground ice are
also known from other northern regions and years
(Riseth et al. 2011; Vikhamar-Schuler et al. 2013;
Rasmus et al. 2016). Ground ice has a negative impact
on vegetation encased in ice (Gudleifsson 2009; Bjerke
et al. 2011; Preece et al. 2012). The winter of 2012 also
led to negative plant responses, similar to those recorded after the 2006 winter (Bjerke et al. 2014). Similar
negative impacts on plants and animals were also reported during the EW20 in the late 1920s and the 1930s
(Langlet 1929; Printz 1933; Bathen 1935; Ruong 1937).
Other years appearing in the top-five list are 1950, 1960,
and 1990. There are indications that weather during
these winters also had negative impacts on plants and
animals (e.g., Solberg 1991; Päiviö 2006; Riseth et al.
2011), but available reports are few.
As discussed earlier, increased occurrence of open
water in the Svalbard region since the winter of 2006
probably caused an amplifying recent warming (Cottier
1 SEPTEMBER 2016
VIKHAMAR-SCHULER ET AL.
6239
FIG. 12. Regionally downscaled scenarios for the season October–April from 1961 to 2100. MD for (a) Ny-Ålesund and (c) Sodankylä.
Precipitation sum for melt and precipitation days (MPDsum) for (b) Ny-Ålesund and (c) Sodankylä. Sodankylä (e) mean temperature and
(f) precipitation sum. Observations as black points and NorACIA RCM data as colored points. ENSEMBLES data as gray shading (outer
light shading showing minimum and maximum values, dark shading showing 10th–90th percentiles, and gray line showing
ensemble mean).
et al. 2007). Warmer ocean temperatures and increasing
frequency of ice-free fjords have led to increasing
northward migration of North Atlantic species (Berge
et al. 2015; Ingvaldsen et al. 2015).
Warmer wetter winters also affect soil temperatures. During the extreme winters of 2006 and 2012,
permafrost soil on Svalbard was much warmer than
during normal winters, and the thermal response was
detectable to a depth of 5–15 m (Isaksen et al. 2007;
Hansen et al. 2014). Multiannual simulations by
Westermann et al. (2011) project that ROS events can
significantly accelerate the warming of soil temperatures in permafrost areas and that initially stable
permafrost systems may in certain areas start to thaw
if environmental conditions such as those observed on
Svalbard in 2006 and 2012 return for several consecutive winters. Resulting increases in active-layer
thickness may in certain areas be associated with unprecedented thaw settlement as ice-rich soils in the
upper-permafrost-layer melt (Nelson et al. 2001),
leading to a marked increase in slope instability
(Harris et al. 2001).
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Warmer wetter winters will affect human societies in
various ways. Extreme winters have proven that ROS
events followed by ground-ice accumulation lead to
more road icings, airport closures, bone fractures,
damage to buildings due to burst from frozen pipes, and
reduced profits from nature-based industries due to
winterkill of reindeer and agricultural grasslands (Helle
and Säntii 1982; Riseth et al. 2011; Vikhamar-Schuler
et al. 2013; Hansen et al. 2014; Bjerke et al. 2014, 2015;
Rasmus et al. 2016; Bokhorst et al. 2016). Higher atmospheric humidity at near-zero temperatures may also
lead to increased icing formation, which can be hazardous to high seas fishery, forestry, and manmade infrastructure (Bulygina et al. 2015). We show here that
warm events will most likely become more frequent in
the NAR in the future, which can in turn have negative
consequences for the winter tourism industry, as reported from the European Alps (Rixen et al. 2011),
northern Sweden (Moen and Fredman 2007), and
Svalbard (Hansen et al. 2014). Moreover, most local
residents prefer some snow during the polar night, as it
lightens up the landscape. Northern residents tend to be
more depressive and have more sleep disorders during
the darkest period of winter (Hansen et al. 1998;
Johnsen et al. 2012), which may become more frequent
during rainy winters.
6. Conclusions
In this study, we analyze changes in the intensity and
frequency of extraordinarily warm weather episodes
occurring during the winter season for the Nordic arctic
region. Long-term trends were evaluated for a set of
climate indices aimed to capture these individual events
based on data from the longest available observational time series in the region, as well as future climate scenarios based on regional climate model
simulations. The main findings are as follows:
d
d
d
Weak positive trends in winter warming events are
found over the past 120 years for the arctic mainland
stations, Karasjok (significant for all indices) and
Tromsø.
Weak positive trends in winter warming events are
found over the past 90 years as well. This is due to the
warm winters of the 1920s and 1930s, the period most
similar to the present (2000–14) in terms of winter
climate. Midwinter warming events were more frequent during the 1920s and 1930s, similarly to the
present. However, early and late winter have a clearer
increasing trend over the past 90 years.
For the past 50 years, we find strong increasing trends in
occurrence and intensity for all climate indices, all statistically significant. The main pattern follows the general
d
d
VOLUME 29
temperature development of arctic areas. The trends are
stronger to the north, with the largest trends at the arctic
islands. Here, the rate of melt days (index 2) in winter
increased by up to 9.2 days decade21. For melt days with
precipitation (mainly as rain; index 5) the increase in
precipitation sum is up to 20.8 mm decade21 (for the
arctic island Jan Mayen).
During the past 15 winters (2000–14), warming events
have become more frequent and intense, seen in a
90-yr perspective. Of the top five extreme winter
warming years, 37% occurred within the most recent
15-yr period, followed by the 15-yr period from 1925 to
1939, which had 20% of the top five extreme winter
warming years.
Regional climate model simulations over the next
50–100 years indicate that the strong past 50-yr trends
will continue. In northern Scandinavia, simulations
indicate a doubling in the number of warming events
while the arctic islands may see a threefold increase
in the number compared to the 1985–2014 reference
period.
These results are valid for stations in the NAR and
hence obtained from a dataset of limited spatial coverage. To evaluate the representativeness of this analysis in
a broader geographical perspective, it is recommended
that a similar study is carried out using reanalysis data
for the entire panarctic region, elucidating possible
variability in extreme events occurring in continental
and maritime climates of the arctic region. We also
suggest carrying out this study with a larger ensemble of
regionally downscaled projections covering the Arctic
(e.g., CORDEX Arctic). A larger spread of simulations
will provide even higher trust to the findings of future
changes.
The findings in this study have improved our understanding of the regional climate variability in the
NAR. The information is important for predicting the
impact of climate change in the arctic environments, as
well as for developing adaptation strategies to deal with
these changes.
Acknowledgments. This study was funded by the
Research Council of Norway (EWWA project,
CONTRACT 216434/E10), by the Polish-Norwegian Programme of the EEA Norway Grants (WICLAP project
ID 198571), and by FRAM–High North Research
Centre for Climate and the Environment through its
terrestrial flagship program. We also thank our colleagues Øyvind Nordli, MET Norway; Abdelkader
Mezgani, MET Norway; and Bruce Rolstad Denby,
MET Norway for valuable comments and suggestions.
The authors gratefully acknowledge two anonymous
reviewers and Jack Kohler, Norwegian Polar Institute,
1 SEPTEMBER 2016
VIKHAMAR-SCHULER ET AL.
also a reviewer, for a significant contribution to improving the manuscript. The Abisko weather station
data were obtained from Annika Kristoffersson at the
Abisko Scientific Research Station, while the Hornsund
weather station data were obtained from the Polish
Academy of Sciences.
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